Garbage collection optimization for non uniform memory access architectures

Cache-coherent non uniform memory access (ccNUMA) architecture is a standard design pattern for contemporary multicore processors, and future generations of architectures are likely to be NUMA. NUMA architectures create new challenges for managed runtime systems. Memory-intensive applications use the system’s distributed memory banks to allocate data, and the automatic memory manager collects garbage left in these memory banks. The garbage collector may need to access remote memory banks, which entails access latency overhead and potential bandwidth saturation for the interconnection between memory banks. This dissertation makes five significant contributions to garbage collection on NUMA systems, with a case study implementation using the Hotspot Java Virtual Machine. It empirically studies data locality for a Stop-The-World garbage collector when tracing connected objects in NUMA heaps. First, it identifies a locality richness which exists naturally in connected objects that contain a root object and its reachable set— ‘rooted sub-graphs’. Second, this dissertation leverages the locality characteristic of rooted sub-graphs to develop a new NUMA-aware garbage collection mechanism. A garbage collector thread processes a local root and its reachable set, which is likely to have a large number of objects in the same NUMA node. Third, a garbage collector thread steals references from sibling threads that run on the same NUMA node to improve data locality. This research evaluates the new NUMA-aware garbage collector using seven benchmarks of an established real-world DaCapo benchmark suite. In addition, evaluation involves a widely used SPECjbb benchmark and Neo4J graph database Java benchmark, as well as an artificial benchmark. The results of the NUMA-aware garbage collector on a multi-hop NUMA architecture show an average of 15% performance improvement. Furthermore, this performance gain is shown to be as a result of an improved NUMA memory access in a ccNUMA system. Fourth, the existing Hotspot JVM adaptive policy for configuring the number of garbage collection threads is shown to be suboptimal for current NUMA machines. The policy uses outdated assumptions and it generates a constant thread count. In fact, the Hotspot JVM still uses this policy in the production version. This research shows that the optimal number of garbage collection threads is application-specific and configuring the optimal number of garbage collection threads yields better collection throughput than the default policy. Fifth, this dissertation designs and implements a runtime technique, which involves heuristics from dynamic collection behavior to calculate an optimal number of garbage collector threads for each collection cycle. The results show an average of 21% improvements to the garbage collection performance for DaCapo benchmarks

Effective memory management for mobile environments

Smartphones, tablets, and other mobile devices exhibit vastly different constraints compared to regular or classic computing environments like desktops, laptops, or servers. Mobile devices run dozens of so-called “apps” hosted by independent virtual machines (VM). All these VMs run concurrently and each VM deploys purely local heuristics to organize resources like memory, performance, and power. Such a design causes conflicts across all layers of the software stack, calling for the evaluation of VMs and the optimization techniques specific for mobile frameworks. In this dissertation, we study the design of managed runtime systems for mobile platforms. More specifically, we deepen the understanding of interactions between garbage collection (GC) and system layers. We develop tools to monitor the memory behavior of Android-based apps and to characterize GC performance, leading to the development of new techniques for memory management that address energy constraints, time performance, and responsiveness. We implement a GC-aware frequency scaling governor for Android devices. We also explore the tradeoffs of power and performance in vivo for a range of realistic GC variants, with established benchmarks and real applications running on Android virtual machines. We control for variation due to dynamic voltage and frequency scaling (DVFS), Just-in-time (JIT) compilation, and across established dimensions of heap memory size and concurrency. Finally, we provision GC as a global service that collects statistics from all running VMs and then makes an informed decision that optimizes across all them (and not just locally), and across all layers of the stack. Our evaluation illustrates the power of such a central coordination service and garbage collection mechanism in improving memory utilization, throughput, and adaptability to user activities. In fact, our techniques aim at a sweet spot, where total on-chip energy is reduced (20–30%) with minimal impact on throughput and responsiveness (5–10%). The simplicity and efficacy of our approach reaches well beyond the usual optimization techniques

Subheap-Augmented Garbage Collection

Automated memory management avoids the tedium and danger of manual techniques. However, as no programmer input is required, no widely available interface exists to permit principled control over sometimes unacceptable performance costs. This dissertation explores the idea that performance-oriented languages should give programmers greater control over where and when the garbage collector (GC) expends effort. We describe an interface and implementation to expose heap partitioning and collection decisions without compromising type safety. We show that our interface allows the programmer to encode a form of reference counting using Hayes\u27 notion of key objects. Preliminary experimental data suggests that our proposed mechanism can avoid high overheads suffered by tracing collectors in some scenarios, especially with tight heaps. However, for other applications, the costs of applying subheaps---in human effort and runtime overheads---remain daunting

Internet of Things and Neural Network Based Energy Optimization and Predictive Maintenance Techniques in Heterogeneous Data Centers

Rapid growth of cloud-based systems is accelerating growth of data centers. Private and public cloud service providers are increasingly deploying data centers all around the world. The need for edge locations by cloud computing providers has created large demand for leasing space and power from midsize data centers in smaller cities. Midsize data centers are typically modular and heterogeneous demanding 100% availability along with high service level agreements. Data centers are recognized as an increasingly troublesome percentage of electricity consumption. Growing energy costs and environmental responsibility have placed the data center industry, particularly midsize data centers under increasing pressure to improve its operational efficiency. The power consumption is mainly due to servers and networking devices on computing side and cooling systems on the facility side. The facility side systems have complex interactions with each other. The static control logic and high number of configuration and nonlinear interdependency create challenges in understanding and optimizing energy efficiency. Doing analytical or experimental approach to determine optimum configuration is very challenging however, a learning based approach has proven to be effective for optimizing complex operations. Machine learning methodologies have proven to be effective for optimizing complex systems. In this thesis, we utilize a learning engine that learns from operationally collected data to accurately predict Power Usage Effectiveness (PUE) and creation of intelligent method to validate and test results. We explore new techniques on how to design and implement Internet of Things (IoT) platform to collect, store and analyze data. First, we study using machine learning framework to predictively detect issues in facility side systems in a modular midsize data center. We propose ways to recognize gaps between optimal values and operational values to identify potential issues. Second, we study using machine learning techniques to optimize power usage in facility side systems in a modular midsize data center. We have experimented with neural network controllers to further optimize the data suite cooling system energy consumption in real time. We designed, implemented, and deployed an Internet of Things framework to collect relevant information from facility side infrastructure. We designed flexible configuration controllers to connect all facility side infrastructure within data center ecosystem. We addressed resiliency by creating reductant controls network and mission critical alerting via edge device. The data collected was also used to enhance service processes that improved operational service level metrics. We observed high impact on service metrics with faster response time (increased 77%) and first time resolution went up by 32%. Further, our experimental results show that we can predictively identify issues in the cooling systems. And, the anomalies in the systems can be identified 30 days to 60 days ahead. We also see the potential to optimize power usage efficiency in the range of 3% to 6%. In the future, more samples of issues and corrective actions can be analyzed to create practical implementation of neural network based controller for real-time optimization.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136074/1/Final Dissertation Vishal Singh.pdfDescription of Final Dissertation Vishal Singh.pdf : Dissertatio

High Performance Reference Counting and Conservative Garbage Collection

Garbage collection is an integral part of modern programming languages. It automatically reclaims memory occupied by objects that are no longer in use. Garbage collection began in 1960 with two algorithmic branches — tracing and reference counting. Tracing identifies live objects by performing a transitive closure over the object graph starting with the stacks, registers, and global variables as roots. Objects not reached by the trace are implicitly dead, so the collector reclaims them. In contrast, reference counting explicitly identifies dead objects by counting the number of incoming references to each object. When an object’s count goes to zero, it is unreachable and the collector may reclaim it. Garbage collectors require knowledge of every reference to each object, whether the reference is from another object or from within the runtime. The runtime provides this knowledge either by continuously keeping track of every change to each reference or by periodically enumerating all references. The collector implementation faces two broad choices — exact and conservative. In exact garbage collection, the compiler and runtime system precisely identify all references held within the runtime including those held within stacks, registers, and objects. To exactly identify references, the runtime must introspect these references during execution, which requires support from the compiler and significant engineering effort. On the contrary, conservative garbage collection does not require introspection of these references, but instead treats each value ambiguously as a potential reference. Highly engineered, high performance systems conventionally use tracing and exact garbage collection. However, other well-established but less performant systems use either reference counting or conservative garbage collection. Reference counting has some advantages over tracing such as: a) it is easier implement, b) it reclaims memory immediately, and c) it has a local scope of operation. Conservative garbage collection is easier to implement compared to exact garbage collection because it does not require compiler cooperation. Because of these advantages, both reference counting and conservative garbage collection are widely used in practice. Because both suffer significant performance overheads, they are generally not used in performance critical settings. This dissertation carefully examines reference counting and conservative garbage collection to understand their behavior and improve their performance. My thesis is that reference counting and conservative garbage collection can perform as well or better than the best performing garbage collectors. The key contributions of my thesis are: 1) An in-depth analysis of the key design choices for reference counting. 2) Novel optimizations guided by that analysis that significantly improve reference counting performance and make it competitive with a well tuned tracing garbage collector. 3) A new collector, RCImmix, that replaces the traditional free-list heap organization of reference counting with a line and block heap structure, which improves locality, and adds copying to mitigate fragmentation. The result is a collector that outperforms a highly tuned production generational collector. 4) A conservative garbage collector based on RCImmix that matches the performance of a highly tuned production generational collector. Reference counting and conservative garbage collection have lived under the shadow of tracing and exact garbage collection for a long time. My thesis focuses on bringing these somewhat neglected branches of garbage collection back to life in a high performance setting and leads to two very surprising results: 1) a new garbage collector based on reference counting that outperforms a highly tuned production generational tracing collector, and 2) a variant that delivers high performance conservative garbage collection

Efficient and elastic management of computing infrastructures

Tesis por compendio[EN] Modern data centers integrate a lot of computer and electronic devices. However, some reports state that the mean usage of a typical data center is around 50% of its peak capacity, and the mean usage of each server is between 10% and 50%. A lot of energy is destined to power on computer hardware that most of the time remains idle. Therefore, it would be possible to save energy simply by powering off those parts from the data center that are not actually used, and powering them on again as they are needed. Most data centers have computing clusters that are used for intensive computing, recently evolving towards an on-premises Cloud service model. Despite the use of low consuming components, higher energy savings can be achieved by dynamically adapting the system to the actual workload. The main approach in this case is the usage of energy saving criteria for scheduling the jobs or the virtual machines into the working nodes. The aim is to power off idle servers automatically. But it is necessary to schedule the power management of the servers in order to minimize the impact on the end users and their applications. The objective of this thesis is the elastic and efficient management of cluster infrastructures, with the aim of reducing the costs associated to idle components. This objective is addressed by automating the power management of the working nodes in a computing cluster, and also proactive stimulating the load distribution to achieve idle resources that could be powered off by means of memory overcommitment and live migration of virtual machines. Moreover, this automation is of interest for virtual clusters, as they also suffer from the same problems. While in physical clusters idle working nodes waste energy, in the case of virtual clusters that are built from virtual machines, the idle working nodes can waste money in commercial Clouds or computational resources in an on-premises Cloud.[ES] En los Centros de Procesos de Datos (CPD) existe una gran concentración de dispositivos informáticos y de equipamiento electrónico. Sin embargo, algunos estudios han mostrado que la utilización media de los CPD está en torno al 50%, y que la utilización media de los servidores se encuentra entre el 10% y el 50%. Estos datos evidencian que existe una gran cantidad de energía destinada a alimentar equipamiento ocioso, y que podríamos conseguir un ahorro energético simplemente apagando los componentes que no se estén utilizando. En muchos CPD suele haber clusters de computadores que se utilizan para computación de altas prestaciones y para la creación de Clouds privados. Si bien se ha tratado de ahorrar energía utilizando componentes de bajo consumo, también es posible conseguirlo adaptando los sistemas a la carga de trabajo en cada momento. En los últimos años han surgido trabajos que investigan la aplicación de criterios energéticos a la hora de seleccionar en qué servidor, de entre los que forman un cluster, se debe ejecutar un trabajo o alojar una máquina virtual. En muchos casos se trata de conseguir equipos ociosos que puedan ser apagados, pero habitualmente se asume que dicho apagado se hace de forma automática, y que los equipos se encienden de nuevo cuando son necesarios. Sin embargo, es necesario hacer una planificación de encendido y apagado de máquinas para minimizar el impacto en el usuario final. En esta tesis nos planteamos la gestión elástica y eficiente de infrastructuras de cálculo tipo cluster, con el objetivo de reducir los costes asociados a los componentes ociosos. Para abordar este problema nos planteamos la automatización del encendido y apagado de máquinas en los clusters, así como la aplicación de técnicas de migración en vivo y de sobreaprovisionamiento de memoria para estimular la obtención de equipos ociosos que puedan ser apagados. Además, esta automatización es de interés para los clusters virtuales, puesto que también sufren el problema de los componentes ociosos, sólo que en este caso están compuestos por, en lugar de equipos físicos que gastan energía, por máquinas virtuales que gastan dinero en un proveedor Cloud comercial o recursos en un Cloud privado.[CA] En els Centres de Processament de Dades (CPD) hi ha una gran concentració de dispositius informàtics i d'equipament electrònic. No obstant això, alguns estudis han mostrat que la utilització mitjana dels CPD està entorn del 50%, i que la utilització mitjana dels servidors es troba entre el 10% i el 50%. Estes dades evidencien que hi ha una gran quantitat d'energia destinada a alimentar equipament ociós, i que podríem aconseguir un estalvi energètic simplement apagant els components que no s'estiguen utilitzant. En molts CPD sol haver-hi clusters de computadors que s'utilitzen per a computació d'altes prestacions i per a la creació de Clouds privats. Si bé s'ha tractat d'estalviar energia utilitzant components de baix consum, també és possible aconseguir-ho adaptant els sistemes a la càrrega de treball en cada moment. En els últims anys han sorgit treballs que investiguen l'aplicació de criteris energètics a l'hora de seleccionar en quin servidor, d'entre els que formen un cluster, s'ha d'executar un treball o allotjar una màquina virtual. En molts casos es tracta d'aconseguir equips ociosos que puguen ser apagats, però habitualment s'assumix que l'apagat es fa de forma automàtica, i que els equips s'encenen novament quan són necessaris. No obstant això, és necessari fer una planificació d'encesa i apagat de màquines per a minimitzar l'impacte en l'usuari final. En esta tesi ens plantegem la gestió elàstica i eficient d'infrastructuras de càlcul tipus cluster, amb l'objectiu de reduir els costos associats als components ociosos. Per a abordar este problema ens plantegem l'automatització de l'encesa i apagat de màquines en els clusters, així com l'aplicació de tècniques de migració en viu i de sobreaprovisionament de memòria per a estimular l'obtenció d'equips ociosos que puguen ser apagats. A més, esta automatització és d'interés per als clusters virtuals, ja que també patixen el problema dels components ociosos, encara que en este cas estan compostos per, en compte d'equips físics que gasten energia, per màquines virtuals que gasten diners en un proveïdor Cloud comercial o recursos en un Cloud privat.Alfonso Laguna, CD. (2015). Efficient and elastic management of computing infrastructures [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/57187Compendi